An AI product manager decides what AI-powered features to build and how to judge them, balancing user needs with model capabilities, data, evaluation and risk. They define success when outputs are probabilistic rather than fixed.
AI governance is the set of policies, roles and controls an organisation uses to decide how AI systems are built, approved, monitored and retired. It is how responsible AI principles get enforced.
An evaluation set is a fixed collection of inputs with expected outcomes, used to score an AI system the same way every time it changes. It plays the role a regression test suite plays for ordinary code.
A product manager decides what a team should build and why, owning the product's direction, priorities and outcomes. Companies hire one when engineering capacity is growing faster than clarity about which problems deserve it.
A prompt engineer designs, tests and refines the instructions and context given to AI models so they produce accurate, consistent and safe output. For buyers, the key question is whether that work needs a dedicated role or sits within AI engineering.
An AI engineer builds software products and features on top of AI models, especially large language models, using prompting, retrieval, tool use and evaluation. Companies hire one to turn a promising AI demo into a dependable product.
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